US10608968B2 - Identifying different chat topics in a communication channel using cognitive data science - Google Patents
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Definitions
- the present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for identifying different chat topics in a communication channel or chatroom using cognitive data science.
- Online chat may refer to any kind of communication over the Internet that offers a real-time transmission of text messages from sender to receiver. Chat messages are generally short in order to enable other participants to respond quickly. Thereby, a feeling similar to a spoken conversation is created, which distinguishes chatting from other text-based online communication forms such as Internet forums and email. Online chat may address point-to-point communications as well as multicast communications from one sender to many receivers and voice and video chat, or may be a feature of a web conferencing service.
- Online chat in a less stringent definition may be primarily any direct text-based or video-based, one-on-one chat or one-to-many or many-to-many group chat (also known as chat room or channel), using tools such as instant messengers, Internet Relay Chat (IRC), talkers, and possibly multi-user dungeons (MUDs).
- IRC Internet Relay Chat
- MODs multi-user dungeons
- chat room is primarily used to describe any form of synchronous conferencing, occasionally even asynchronous conferencing.
- the term can thus mean any technology ranging from real-time online chat and online interaction with strangers (e.g., online forums) to fully immersive graphical social environments.
- the primary use of a chat room is to share information via text with a group of other users.
- chat rooms from instant messaging programs, which are more typically designed for one-to-one communication.
- the users in a particular chat room are generally connected via a shared internet or other similar connection, and chat rooms exist catering for a wide range of subjects.
- Collaborative software or groupware is application software designed to help people involved in a common task to achieve their goals.
- collaborative software may be divided into: real-time collaborative editing (RTCE) platforms that allow multiple users to engage in live, simultaneous, and reversible editing of a single file (usually a document), and version control (also known as revision control and source control) platforms, which allow separate users to make parallel edits to a file, while preserving every saved edit by every user as multiple files that are variants of the original file.
- RTCE real-time collaborative editing
- version control also known as revision control and source control
- Collaborative software is a broad concept that overlaps considerably with computer-supported cooperative work (CSCW).
- a method in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions which are executed by the at least one processor and configure the processor to implement a chat topic identification system for identifying chat topics in a multi-user messaging platform.
- the method comprises receiving, by the chat topic identification system, a plurality of chat messages submitted to a communication channel in the multi-user messaging platform.
- the method further comprises performing, by a cognitive content language analysis component executing within the chat topic identification system, cognitive content language analysis on at least one given chat message to determine relevance among words, to identify separate topic indicator words, and to build topic domains likely to be relevant to participants of the communication channel.
- the method further comprises performing, by a cognitive personality analysis component executing within the chat topic identification system, cognitive personality analysis on the at least one given chat message to determine a personality type associated with the at least one given chat message.
- the method further comprises performing, by a cognitive tone analysis component executing within the chat topic identification system, cognitive tone analysis on the at least one given chat message to determine a tone category associated with the at least one given chat message.
- the method further comprises performing, by a social data analysis component executing within the chat topic identification system, social data analysis to enhance a user profile associated with the at least one given chat message.
- the method further comprises performing, by a conversation feature analysis component executing within the chat topic identification system, conversation feature analysis to determine conversation features associated with the at least one given chat message.
- the method further comprises storing, by the chat topic identification system, results of the cognitive content language analysis, the cognitive personality analysis, the cognitive tone analysis, and the social data analysis in a user profile associated with the user to form an updated user profile.
- the method further comprises determining, by a new conversation prediction engine executing within the chat topic identification system, a conversation separation score representing likelihood that a new conversation is being started in the communication channel. The conversation separation score is generated based on analysis of the updated user profile containing previous cognitive analysis results from cognitive content language analysis, the cognitive personality analysis, the cognitive tone analysis, and the social data analysis.
- the method further comprises generating, by a new conversation separation recommendation engine executing within the chat topic identification system, a conversation separation recommendation based on the conversation separation score and results of the cognitive content language analysis, the cognitive personality analysis, the cognitive tone analysis, the social data analysis, and the conversation feature analysis.
- a computer program product comprising a computer useable or readable medium having a computer readable program.
- the computer readable program when executed on a computing device, causes the computing device to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.
- a system/apparatus may comprise one or more processors and a memory coupled to the one or more processors.
- the memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.
- FIG. 1 is an example diagram of a distributed data processing system in which aspects of the illustrative embodiments may be implemented;
- FIG. 2 is an example block diagram of a computing device in which aspects of the illustrative embodiments may be implemented
- FIG. 3 is a block diagram illustrating a mechanism for identifying different chat topics in a communication channel in accordance with an illustrative embodiment
- FIG. 4 is a flowchart illustrating operation of a mechanism for identifying different chat topics in a communication channel using cognitive data science methods in accordance with an illustrative embodiment.
- the illustrative embodiments provide a mechanism for cognitively separating conversation topics/threads in one communication channel using multiple layer data science processes and various data science methods.
- Data science is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, structured or unstructured, similar to data mining. Data science unifies statistics, data analysis, and related methods in order to analyze actual phenomena with data. Data science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.
- the mechanism performs feature collection as follows:
- Cognitive content language analysis to get relevance scores among words, to identify separate topic indicator words, and to build topic domains that include all of the words that users responded to in a user profile.
- Cognitive personality analysis to assign a score of likelihood to start a new conversation thread based on a user's personality features.
- Cognitive tone analysis to assign a score of likelihood to start a new conversation thread based on a tone category of a user's language.
- Conversation feature analysis to determine relationships of users, historical time period, time of the chat, and other system information.
- the mechanism performs cognitive function speech to text if voice message are being used in the communication channel.
- the mechanism repeats the feature collection above for language and content analysis and personality analysis.
- the mechanism performs new conversation prediction.
- the mechanism uses all of the above features as inputs for data science classification algorithms to predict the final likelihood of a new conversation thread.
- the mechanism provides new conversation separation recommendations. Based on the final new conversation result based on the above steps, the mechanism recommends a separation of the new conversation.
- the mechanism may display the separated conversation in a separate communication channel or color code the conversation threads, for example.
- the mechanism pulls all of the engaged participants as well as the relevant conversation messages into a new conversation thread.
- a multi-user platform may be the Slack platform.
- Slack is a cloud-based set of team collaboration tools and services, founded by Stewart Butterfield. The name is an acronym for “Searchable Log of All Conversation and Knowledge.”
- SLACK is a registered trademark of Slack Technologies, Inc.
- a first Slack channel for communication regarding a main topic may be established for a group of thirty users. At a particular point in time, there may be twenty different users who have collectively posted several hundred messages that relate to different aspects of the main topic. Three of the twenty users may have exchanged six messages regarding a particular aspect or subtopic of the main topic. According to various embodiments, a second Slack channel may be automatically recommended for the subtopic being discussed by the three users.
- the messages initiated by each of the three users are reproduced in or copied over into the second Slack channel.
- the second channel allows the three users to see their subtopic conversation without the clutter of the hundreds of messages in the first Slack channel.
- a “mechanism” will be used to refer to elements of the present invention that perform various operations, functions, and the like.
- a “mechanism,” as the term is used herein, may be an implementation of the functions or aspects of the illustrative embodiments in the form of an apparatus, a procedure, or a computer program product. In the case of a procedure, the procedure is implemented by one or more devices, apparatus, computers, data processing systems, or the like.
- the logic represented by computer code or instructions embodied in or on the computer program product is executed by one or more hardware devices in order to implement the functionality or perform the operations associated with the specific “mechanism.”
- the mechanisms described herein may be implemented as specialized hardware, software executing on general purpose hardware, software instructions stored on a medium such that the instructions are readily executable by specialized or general purpose hardware, a procedure or method for executing the functions, or a combination of any of the above.
- an engine if used herein with regard to describing embodiments and features of the invention, is not intended to be limiting of any particular implementation for accomplishing and/or performing the actions, steps, processes, etc., attributable to and/or performed by the engine.
- An engine may be, but is not limited to, software, hardware and/or firmware or any combination thereof that performs the specified functions including, but not limited to, any use of a general and/or specialized processor in combination with appropriate software loaded or stored in a machine readable memory and executed by the processor.
- any name associated with a particular engine is, unless otherwise specified, for purposes of convenience of reference and not intended to be limiting to a specific implementation.
- any functionality attributed to an engine may be equally performed by multiple engines, incorporated into and/or combined with the functionality of another engine of the same or different type, or distributed across one or more engines of various configurations.
- FIGS. 1 and 2 are provided hereafter as example environments in which aspects of the illustrative embodiments may be implemented. It should be appreciated that FIGS. 1 and 2 are only examples and are not intended to assert or imply any limitation with regard to the environments in which aspects or embodiments of the present invention may be implemented. Many modifications to the depicted environments may be made without departing from the spirit and scope of the present invention.
- FIG. 1 depicts a pictorial representation of an example distributed data processing system in which aspects of the illustrative embodiments may be implemented.
- Distributed data processing system 100 may include a network of computers in which aspects of the illustrative embodiments may be implemented.
- the distributed data processing system 100 contains at least one network 102 , which is the medium used to provide communication links between various devices and computers connected together within distributed data processing system 100 .
- the network 102 may include connections, such as wire, wireless communication links, or fiber optic cables.
- server 104 and server 106 are connected to network 102 along with storage unit 108 .
- clients 110 , 112 , and 114 are also connected to network 102 .
- These clients 110 , 112 , and 114 may be, for example, personal computers, network computers, or the like.
- server 104 provides data, such as boot files, operating system images, and applications to the clients 110 , 112 , and 114 .
- Clients 110 , 112 , and 114 are clients to server 104 in the depicted example.
- Distributed data processing system 100 may include additional servers, clients, and other devices not shown.
- distributed data processing system 100 is the Internet with network 102 representing a worldwide collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP) suite of protocols to communicate with one another.
- TCP/IP Transmission Control Protocol/Internet Protocol
- the distributed data processing system 100 may also be implemented to include a number of different types of networks, such as for example, an intranet, a local area network (LAN), a wide area network (WAN), or the like.
- FIG. 1 is intended as an example, not as an architectural limitation for different embodiments of the present invention, and therefore, the particular elements shown in FIG. 1 should not be considered limiting with regard to the environments in which the illustrative embodiments of the present invention may be implemented.
- one or more of the computing devices may be specifically configured to implement a mechanism for identifying different chat topics in a communication channel or chatroom using cognitive data science.
- the configuring of the computing device may comprise the providing of application specific hardware, firmware, or the like to facilitate the performance of the operations and generation of the outputs described herein with regard to the illustrative embodiments.
- the configuring of the computing device may also, or alternatively, comprise the providing of software applications stored in one or more storage devices and loaded into memory of a computing device, such as server 104 , for causing one or more hardware processors of the computing device to execute the software applications that configure the processors to perform the operations and generate the outputs described herein with regard to the illustrative embodiments.
- a computing device such as server 104
- any combination of application specific hardware, firmware, software applications executed on hardware, or the like may be used without departing from the spirit and scope of the illustrative embodiments.
- the computing device becomes a specialized computing device specifically configured to implement the mechanisms of the illustrative embodiments and is not a general purpose computing device.
- the implementation of the mechanisms of the illustrative embodiments improves the functionality of the computing device and provides a useful and concrete result that facilitates identifying different chat topics in a communication channel.
- FIG. 2 is a block diagram of just one example data processing system in which aspects of the illustrative embodiments may be implemented.
- Data processing system 200 is an example of a computer, such as server 104 in FIG. 1 , in which computer usable code or instructions implementing the processes and aspects of the illustrative embodiments of the present invention may be located and/or executed so as to achieve the operation, output, and external effects of the illustrative embodiments as described herein.
- data processing system 200 employs a hub architecture including north bridge and memory controller hub (NB/MCH) 202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204 .
- NB/MCH north bridge and memory controller hub
- I/O input/output controller hub
- Processing unit 206 , main memory 208 , and graphics processor 210 are connected to NB/MCH 202 .
- Graphics processor 210 may be connected to NB/MCH 202 through an accelerated graphics port (AGP).
- AGP accelerated graphics port
- local area network (LAN) adapter 212 connects to SB/ICH 204 .
- Audio adapter 216 , keyboard and mouse adapter 220 , modem 222 , read only memory (ROM) 224 , hard disk drive (HDD) 226 , CD-ROM drive 230 , universal serial bus (USB) ports and other communication ports 232 , and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus 240 .
- PCI/PCIe devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not.
- ROM 224 may be, for example, a flash basic input/output system (BIOS).
- HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240 .
- HDD 226 and CD-ROM drive 230 may use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface.
- IDE integrated drive electronics
- SATA serial advanced technology attachment
- Super I/O (SIO) device 236 may be connected to SB/ICH 204 .
- An operating system runs on processing unit 206 .
- the operating system coordinates and provides control of various components within the data processing system 200 in FIG. 2 .
- the operating system may be a commercially available operating system such as Microsoft® Windows 7®.
- An object-oriented programming system such as the JavaTM programming system, may run in conjunction with the operating system and provides calls to the operating system from JavaTM programs or applications executing on data processing system 200 .
- data processing system 200 may be, for example, an IBM eServerTM System p® computer system, PowerTM processor based computer system, or the like, running the Advanced Interactive Executive (AIX®) operating system or the LINUX® operating system.
- Data processing system 200 may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit 206 . Alternatively, a single processor system may be employed.
- SMP symmetric multiprocessor
- Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as HDD 226 , and may be loaded into main memory 208 for execution by processing unit 206 .
- the processes for illustrative embodiments of the present invention may be performed by processing unit 206 using computer usable program code, which may be located in a memory such as, for example, main memory 208 , ROM 224 , or in one or more peripheral devices 226 and 230 , for example.
- a bus system such as bus 238 or bus 240 as shown in FIG. 2 , may be comprised of one or more buses.
- the bus system may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture.
- a communication unit such as modem 222 or network adapter 212 of FIG. 2 , may include one or more devices used to transmit and receive data.
- a memory may be, for example, main memory 208 , ROM 224 , or a cache such as found in NB/MCH 202 in FIG. 2 .
- the mechanisms of the illustrative embodiments may be implemented as application specific hardware, firmware, or the like, application software stored in a storage device, such as HDD 226 and loaded into memory, such as main memory 208 , for executed by one or more hardware processors, such as processing unit 206 , or the like.
- the computing device shown in FIG. 2 becomes specifically configured to implement the mechanisms of the illustrative embodiments and specifically configured to perform the operations and generate the outputs described hereafter with regard to the mechanism for identifying different chat topics in a communication channel.
- FIGS. 1 and 2 may vary depending on the implementation.
- Other internal hardware or peripheral devices such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in FIGS. 1 and 2 .
- the processes of the illustrative embodiments may be applied to a multiprocessor data processing system, other than the SMP system mentioned previously, without departing from the spirit and scope of the present invention.
- data processing system 200 may take the form of any of a number of different data processing systems including client computing devices, server computing devices, a tablet computer, laptop computer, telephone or other communication device, a personal digital assistant (PDA), or the like.
- data processing system 200 may be a portable computing device that is configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data, for example.
- data processing system 200 may be any known or later developed data processing system without architectural limitation.
- FIG. 3 is a block diagram illustrating a mechanism for identifying different chat topics in a communication channel in accordance with an illustrative embodiment.
- Chat platform 310 receives chat messages 301 .
- Chat platform 310 provides access to the chat messages 301 to chat topic identification system 320 , which includes cognitive content language analysis component 321 , cognitive personality analysis component 322 , cognitive tone analysis component 323 , social data analysis component 324 , and conversation feature analysis component 325 .
- Cognitive content language analysis component 321 determines relevance scores among words, identifies separate topic indicator words, and builds a topic domain that includes all of the words users responded to in the user profile. Separate topic indicator words may include, for example, the following: “by the way,” “another topic,” “PS,” etc.
- Cognitive content language analysis component 321 uses a natural language classifier trained with historical data to determine what keywords or phrases might trigger new conversation threads. Each keyword gets a score of likelihood of starting a new conversation thread.
- One cognitive method is to associate communication channel members with specific words. Thus, cognitive content language analysis component 321 may look at historical chat logs and use the distance between an occurrence of a non-stopword and a user's response. Users with knowledge germane to the topic (word) will be on average closer to that word during conversations.
- cognitive content language analysis component 321 can use this inference to separate that part of the conversation from other conversations.
- Cognitive content language analysis component 321 uses natural language processing (NLP) methods for text analysis to get the relevance between words.
- NLP natural language processing
- Cognitive personality analysis component 322 determines a personality type for each user in the communication channel. For example, cognitive personality analysis component 322 may determine a personality type of “impatient” for a given user. Cognitive personality analysis component 322 may then determines a score representing the likelihood that the personality type will start a new conversation thread.
- Cognitive tone analysis component 323 determines a tone category for each user in the communication channel. For example, cognitive tone analysis component 323 may determine a tone of a given message in the communication channel is “frustrated.” Cognitive tone analysis component 323 may then determine a score representing the likelihood that a message of that tone category will lead to a new conversation thread being started.
- Social data analysis component 324 enhances each user profile with a similarity score. That is, the social analysis can provide the user relationship with other users in the same communication channel. Also, the social data can be analyzed by other cognitive functions, such as sentiment analysis and personality analysis, to predict the likelihood of starting a new conversation. Social data analysis component 324 leverages other social networks to build a more accurate user profile. Social data analysis component 324 may determine connections in other social networks, reposting of other messages on other social networks, and the like.
- Conversation feature analysis component 325 determines other conversation features relevant to predicting whether a new conversation is being started. Conversation feature analysis component 325 may determine the historical time period being discussed, the time of the chat, and the target of messages. In modern communication channels, @user is typically used to target a specific user that you want to talk to directly. So in the historical chat data, a user might have targeted another user to chat via @user, this information can be stored in the user profile and then used for new chat prediction.
- Chat topic identification system 320 may also perform cognitive speech to text if voice messages are being used in the communication channel. Chat topic identification system 320 may then pass the resulting text through one or more of components 321 - 325 for language and content analysis and personality analysis.
- Chat topic identification system 320 stores results of cognitive content language analysis 321 , cognitive personality analysis 322 , cognitive tone analysis 323 , social data analysis 324 , and conversation feature analysis 325 in user profile 302 . Chat topic identification system 320 builds a user profile 302 , which is to be used as an input for new conversation prediction.
- Chat topic identification system 320 also includes new conversation prediction engine 330 , which uses features from components 321 - 325 as inputs for data science classification algorithms to predict the final likelihood of a new conversation thread.
- new conversation prediction engine 330 receives as input content language features, personality analysis features, tone analysis features, social data features, and other conversation features from user profile 302 and generates a prediction score representing the likelihood that a new conversation is being started based on those features.
- New conversation prediction engine 330 may examine historical chat logs and use the distance between an occurrence of a non-stopword and a user's response. Users with knowledge germane to the topic (word) will be on average closer to that word during conversations. Then in real time when that user responds to the word, new conversation prediction engine 330 uses this inference to separate that part of the conversation from other conversations.
- the mechanism may use a Latent Dirichlet Allocation (LDA) algorithm, which uses semantic features to cluster topics.
- LDA Latent Dirichlet Allocation
- the LDA algorithm takes a group of documents and returns a number of topics, which are made up of a number of words, most relevant to these documents.
- LDA is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics.
- each message may be viewed as a document having one or more topics.
- Chat topic identification system 320 also includes new conversation separation recommendation engine 340 . Based on the final new conversation result from components 321 - 325 and new conversation prediction engine 330 , new conversation separation recommendation engine 340 will generate conversation separation recommendation 341 recommending a separation of a new conversation. Conversation separation recommendation 341 may recommend displaying a new conversation in a separate communication channel or color coding the conversation threads, for example. Conversation separation recommendation 341 may also recommend pulling all of the engaged participants as well as the relevant conversation messages into a new conversation thread.
- New conversation separation recommendation engine 340 sends conversation separation recommendation 341 to chat platform 310 .
- a user of chat platform 310 may then act on conversation separation recommendation 341 to separate the engaged participants and relevant conversation messages into a new conversation thread.
- chat platform 310 may then create a new communication channel for the new conversation thread.
- the new communication channel may be prepopulated with the relevant conversation messages and the engaged participants.
- chat platform 310 may color code the separate conversation threads within the same communication channel based on conversation separation recommendation 341 .
- the present invention may be a system, a method, and/or a computer program product.
- the computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
- the computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.
- the computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
- a non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing.
- RAM random access memory
- ROM read-only memory
- EPROM or Flash memory erasable programmable read-only memory
- SRAM static random access memory
- CD-ROM compact disc read-only memory
- DVD digital versatile disk
- memory stick a floppy disk
- a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon
- a computer readable storage medium is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
- Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network.
- the network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
- a network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
- Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages.
- the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
- the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
- electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
- These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
- These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
- the computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
- FIG. 4 is a flowchart illustrating operation of a mechanism for identifying different chat topics in a communication channel using cognitive data science methods in accordance with an illustrative embodiment. Operation begins when a chat message is received in a communication channel (block 400 ). The mechanism performs cognitive function speech to text if voice messages are being used (block 401 ).
- the mechanism performs cognitive content language analysis on the chat message to determine a relevance score among words in the chat, to identify separate topic indicator words, and to build a topic domain (block 402 ).
- the mechanism performs cognitive personality analysis to determine a personality type associated with the chat message (block 403 ).
- the mechanism then performs tone analysis to determine a tone category associated with the chat message (block 404 ).
- the mechanism performs social data analysis (block 405 ). Then, the mechanism stores results of the cognitive content language analysis, the cognitive personality analysis, the tone analysis, and the social data analysis in a user profile (block 406 ).
- the mechanism then predicts a final likelihood that a new conversation is being started based on the contents of the cognitive analysis stored in the user profile (block 407 ).
- the mechanism determines whether the final likelihood indicates that a new conversation is being started (block 408 ).
- the mechanism may determine whether the likelihood that a new conversation is being started is greater than a predetermined threshold. If the final likelihood indicates that a new conversation is being started, then the mechanism recommends a separation of the new conversation and pulls engaged participants and relevant conversation messages into the new conversation thread (block 409 ). Thereafter, operation ends for the current chat message (block 410 ). If the final likelihood does not indicate that a new conversation is being started in block 408 , then operation ends (block 410 ). Operation then repeats for each subsequent chat message in the chat channel.
- each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
- the functions noted in the block may occur out of the order noted in the figures.
- two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
- the illustrative embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements.
- the mechanisms of the illustrative embodiments are implemented in software or program code, which includes but is not limited to firmware, resident software, microcode, etc.
- a data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a communication bus, such as a system bus, for example.
- the memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.
- the memory may be of various types including, but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory, solid state memory, and the like.
- I/O devices can be coupled to the system either directly or through intervening wired or wireless I/O interfaces and/or controllers, or the like.
- I/O devices may take many different forms other than conventional keyboards, displays, pointing devices, and the like, such as for example communication devices coupled through wired or wireless connections including, but not limited to, smart phones, tablet computers, touch screen devices, voice recognition devices, and the like. Any known or later developed I/O device is intended to be within the scope of the illustrative embodiments.
- Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters for wired communications.
- Wireless communication based network adapters may also be utilized including, but not limited to, 802.11 a/b/g/n wireless communication adapters, Bluetooth wireless adapters, and the like. Any known or later developed network adapters are intended to be within the spirit and scope of the present invention.
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